在上一篇文章中，我们已经构建了决策树，接下来可以使用它用于实际的数据分类。在执行数据分类时，需要决策时以及标签向量。程序比较测试数据和决策树上的数值，递归执行直到进入叶子节点。
这篇文章主要使用决策树分类器就行分类，数据集采用UCI数据库中的红酒，白酒数据，主要特征包括12个，主要有非挥发性酸,挥发性酸度, 柠檬酸, 残糖含量,氯化物, 游离二氧化硫, 总二氧化硫,密度, pH,硫酸盐,酒精, 质量等特征。
下面是具体代码的实现：
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
|
#coding :utf-8
'''
2017.6.26 author :Erin
function: "decesion tree" ID3
'''
import numpy as np
import pandas as pd
from math import log
import operator
import random
def load_data():
red = [line.strip().split(';') for line in open('e:/a/winequality-red.csv')]
white = [line.strip().split(';') for line in open('e:/a/winequality-white.csv')]
data=red+white
random.shuffle(data) #打乱data
x_train=data[:800]
x_test=data[800:]
features=['fixed','volatile','citric','residual','chlorides','free','total','density','pH','sulphates','alcohol','quality']
return x_train,x_test,features
def cal_entropy(dataSet):
numEntries = len(dataSet)
labelCounts = {}
for featVec in dataSet:
label = featVec[-1]
if label not in labelCounts.keys():
labelCounts[label] = 0
labelCounts[label] += 1
entropy = 0.0
for key in labelCounts.keys():
p_i = float(labelCounts[key]/numEntries)
entropy -= p_i * log(p_i,2)#log(x,10)表示以10 为底的对数
return entropy
def split_data(data,feature_index,value):
'''
划分数据集
feature_index：用于划分特征的列数，例如“年龄”
value:划分后的属性值：例如“青少年”
'''
data_split=[]#划分后的数据集
for feature in data:
if feature[feature_index]==value:
reFeature=feature[:feature_index]
reFeature.extend(feature[feature_index+1:])
data_split.append(reFeature)
return data_split
def choose_best_to_split(data):
'''
根据每个特征的信息增益，选择最大的划分数据集的索引特征
'''
count_feature=len(data[0])-1#特征个数4
#print(count_feature)#4
entropy=cal_entropy(data)#原数据总的信息熵
#print(entropy)#0.9402859586706309
max_info_gain=0.0#信息增益最大
split_fea_index = -1#信息增益最大，对应的索引号
for i in range(count_feature):
feature_list=[fe_index[i] for fe_index in data]#获取该列所有特征值
#######################################
# print(feature_list)
unqval=set(feature_list)#去除重复
Pro_entropy=0.0#特征的熵
for value in unqval:#遍历改特征下的所有属性
sub_data=split_data(data,i,value)
pro=len(sub_data)/float(len(data))
Pro_entropy+=pro*cal_entropy(sub_data)
#print(Pro_entropy)
info_gain=entropy-Pro_entropy
if(info_gain>max_info_gain):
max_info_gain=info_gain
split_fea_index=i
return split_fea_index
##################################################
def most_occur_label(labels):
#sorted_label_count[0][0] 次数最多的类标签
label_count={}
for label in labels:
if label not in label_count.keys():
label_count[label]=0
else:
label_count[label]+=1
sorted_label_count = sorted(label_count.items(),key = operator.itemgetter(1),reverse = True)
return sorted_label_count[0][0]
def build_decesion_tree(dataSet,featnames):
'''
字典的键存放节点信息，分支及叶子节点存放值
'''
featname = featnames[:] ################
classlist = [featvec[-1] for featvec in dataSet] #此节点的分类情况
if classlist.count(classlist[0]) == len(classlist): #全部属于一类
return classlist[0]
if len(dataSet[0]) == 1: #分完了,没有属性了
return Vote(classlist) #少数服从多数
# 选择一个最优特征进行划分
bestFeat = choose_best_to_split(dataSet)
bestFeatname = featname[bestFeat]
del(featname[bestFeat]) #防止下标不准
DecisionTree = {bestFeatname:{}}
# 创建分支,先找出所有属性值,即分支数
allvalue = [vec[bestFeat] for vec in dataSet]
specvalue = sorted(list(set(allvalue))) #使有一定顺序
for v in specvalue:
copyfeatname = featname[:]
DecisionTree[bestFeatname][v] = build_decesion_tree(split_data(dataSet,bestFeat,v),copyfeatname)
return DecisionTree
def classify(Tree, featnames, X):
classLabel=''
root = list(Tree.keys())[0]
firstDict = Tree[root]
featindex = featnames.index(root) #根节点的属性下标
#classLabel='0'
for key in firstDict.keys(): #根属性的取值,取哪个就走往哪颗子树
if X[featindex] == key:
if type(firstDict[key]) == type({}):
classLabel = classify(firstDict[key],featnames,X)
else:
classLabel = firstDict[key]
return classLabel
if __name__ == '__main__':
x_train,x_test,features=load_data()
split_fea_index=choose_best_to_split(x_train)
newtree=build_decesion_tree(x_train,features)
#print(newtree)
#classLabel=classify(newtree, features, ['7.4','0.66','0','1.8','0.075','13','40','0.9978','3.51','0.56','9.4','5'] )
#print(classLabel)
count=0
for test in x_test:
label=classify(newtree, features,test)
if(label==test[-1]):
count=count+1
acucy=float(count/len(x_test))
print(acucy)
|
测试的准确率大概在0.7左右。至此决策树分类算法结束。本文代码地址
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/momaojia/article/details/73835851